使用matplotlib画简单的图形：
#-*- coding:utf-8 -*-
from numpy.random import randn
import matplotlib.pyplot as plt
fig=plt.figure()
ax1=fig.add_subplot(2,2,1)
plt.plot(randn(50).cumsum(),'k--')
ax2=fig.add_subplot(2,2,2)
#bins越大矩形越窄 alpha表示颜色深度
ax2.hist(randn(10000), bins = 30, color = 'red', alpha = 1)
ax3=fig.add_subplot(2,2,3)
plt.plot([1.5, 2, 4, -2, 1.6])
plt.show()
运行结果：
散点图
#-*- coding:utf-8 -*-
from pylab import *
import matplotlib.pyplot as plt
mpl.rcParams['font.sans-serif'] = ['SimHei']
n = 1024
X = np.random.normal(0,1,n)
Y = np.random.normal(0,1,n)
for i in range(1,10):
scatter(i, i)
plt.title(u"散点图",color='red')
show()
pyplot.subplots有几个选项
nrows：subplot的行数
ncols：subplot的列数
sharex：所有subplot共享x轴刻度
sharey：所有subplot共享Y轴刻度
#-*- coding:utf-8 -*-
from numpy.random import randn
from matplotlib import pyplot as plt
fig,axes=plt.subplots(2,2,sharex=True,sharey=True)
for i in range(2):
for j in range(2):
axes[i,j].hist(randn(50),bins=50,color='red',alpha=1)
plt.show()
矩阵图
#-*- coding:utf-8 -*-
from pylab import *
#使用中文
mpl.rcParams['font.sans-serif'] = ['SimHei']
#显示负号
matplotlib.rcParams['axes.unicode_minus'] = False
n=32
list1=[i for i in range(1,33)]
list2=[i for i in range(-32,0)]
n= np.arange(n)
xlim(-1,32)
ylim(-35,35)
xlabel(u'每个城市招聘人数')
bar(n, list1, facecolor='yellow', edgecolor='white')
bar(n, list2, facecolor='red', edgecolor='white')
for x,y in zip(n,list1):
text(x, y, '%d' % y, ha='center', va= 'bottom' )
for x,y in zip(n,list2):
text(x, y-3, '%d' % y, ha='center', va= 'bottom')
show()
饼图
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' # 设置标签
sizes = [15, 30, 45, 10] # 占比，和为100
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] # 颜色
explode = (0, 0.1, 0, 0) # 展开第二个扇形，即Hogs，间距为0.1
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True,
startangle=90) # startangle控制饼状图的旋转方向
plt.axis('equal') # 保证饼状图是正圆，否则会有一点角度偏斜
plt.show()
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' # 设置标签
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] # 颜色
explode = (0.1, 0.2, 0, 0) # 展开第二个扇形，即Hogs，间距为0.1
fig = plt.figure()
ax = fig.gca()
ax.pie(np.random.random(4), explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True,
startangle=90, radius=0.25, center=(0, 0), frame=True)
ax.pie(np.random.random(4), explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True,
startangle=90, radius=0.25, center=(1, 1), frame=True)
ax.pie(np.random.random(4), explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True,
startangle=90, radius=0.25, center=(0, 1), frame=True)
ax.pie(np.random.random(4), explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True,
startangle=90, radius=0.25, center=(1, 0), frame=True)
ax.set_xticks([0, 1]) # 设置位置
ax.set_yticks([0, 1])
ax.set_xticklabels(["Sunny", "Cloudy"]) # 设置标签
ax.set_yticklabels(["Dry", "Rainy"])
ax.set_xlim((-0.5, 1.5))
ax.set_ylim((-0.5, 1.5))
ax.set_aspect('equal')
plt.show()
热力图
#-*- coding:utf-8 -*-
from pylab import *
def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
n = 256
x = np.linspace(-3,3,n)
y = np.linspace(-3,3,n)
X,Y = np.meshgrid(x,y)
contourf(X, Y, f(X,Y), 8, alpha=.75, cmap='jet')
C = contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)
show()
利用numpy来实现sin函数
#-*- coding:utf-8 -*-
from pylab import *
#使用中文
mpl.rcParams['font.sans-serif'] = ['SimHei']
#显示负号
matplotlib.rcParams['axes.unicode_minus'] = False
t=np.arange(0.0,2.0,0.01)#0到2之间，以0.01为间距
s=np.sin(2*np.pi*t)#利用numpy实现2sinπx
plt.plot(t,s)
plt.xlabel('t的值')
plt.ylabel('s的值')
#这里同时可以使用plt.xlim()和plt.ylim()来限制x、y轴的范围
plt.show()
#-*- coding:utf-8 -*-
from pylab import *
#使用中文
mpl.rcParams['font.sans-serif'] = ['SimHei']
#显示负号
matplotlib.rcParams['axes.unicode_minus'] = False
x1=np.linspace(0.0,5.0)
x2=np.linspace(0.0,2.0)
y1=np.cos(2*np.pi*x1)*np.exp(-x1)
y2=np.cos(2*np.pi*x2)
plt.subplot(2,1,1)
plt.plot(x1,y1,'y*-')#y表示颜色，*表示点的样子，-表示连接
plt.title('图1')
plt.subplot(2,1,2)#最后一个2表示在第二个位置
plt.plot(x1,y2,'r.--')
plt.title('图2')
plt.show()
#-*- coding:utf-8 -*-
from pylab import *
#使用中文
mpl.rcParams['font.sans-serif'] = ['SimHei']
#显示负号
matplotlib.rcParams['axes.unicode_minus'] = False
mu=1000
sigma=15
x=mu+sigma*np.random.randn(10000)#在均值周围产生符合正态分布x值
num_bins=50
n,bins,patches=plt.hist(x,num_bins,normed=1,facecolor='green',alpha=0.5)
#直方图函数，x为x轴的值，normed=1表示为概率密度，即和为一，绿色方块，色深参数0.5.返回n个概率，直方块左边线的x值，及各个方块对象
y=mlab.normpdf(bins,mu,sigma)#画一条逼近的曲线
plt.plot(bins,y,'r--')
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
plt.subplots_adjust(left=0.15)
plt.show()
# -*- coding:utf-8 -*-
from pylab import *
from mpl_toolkits.mplot3d import Axes3D
x_list = [[3, 3, 2], [4, 3, 1], [1, 2, 3], [1, 1, 2], [2, 1, 2]]
fig = plt.figure()
ax = Axes3D(fig)
for x in x_list:
ax.scatter(x[0], x[1], x[2], c='r')
plt.show()
from pylab import *
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')
X = np.arange(1, 10, 1)
Y = np.arange(1, 10, 1)
X, Y = np.meshgrid(X, Y)
Z = 3 * X + 2 * Y + 30
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0, antialiased=True)
ax.set_zlim3d(0,100)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
# -*- coding:utf-8 -*-
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(-5, 5, 0.1)
Y = np.arange(-5, 5, 0.1)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
Z = np.sin(R)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
# 画表面,x,y,z坐标， 横向步长，纵向步长，颜色，线宽，是否渐变
ax.set_zlim(-1.01, 1.01) # 坐标系的下边界和上边界
ax.zaxis.set_major_locator(LinearLocator(10)) # 设置Z轴标度
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) # Z轴精度
fig.colorbar(surf, shrink=0.5, aspect=5) # shrink颜色条伸缩比例（0-1），aspect颜色条宽度（反比例，数值越大宽度越窄）
plt.show()
